Ukrainian President Volodymyr Zelenskyy has said the accreditation of the Iranian ambassador will be revoked and Iranian diplomatic staff in Kyiv reduced as a result of Russian forces using Iranian drones to attack Ukraine. Ukrainian forces had shot down a total of eight Iranian-made drones in the conflict so far, Zelenskyy said in a late-night video address on Friday. "Today the Russian army used Iranian drones for its attacks on Dnipropetrovsk region and Odesa. I instructed the Ministry of Foreign Affairs to strongly react to this fact," Zelenskyy said in his address. "Six of these Iranian drones were downed by our air defences of the East and South commands. One more was brought to ground by air defences of the Navy…. And just now I am being told about the downing by air defences of the South command of another strike Iranian drone," he said.

It's natural to link technology and artificial intelligence to industries like telecommunications, marketing, and manufacturing. Clients still receive cards in the mail, meet with agents in their offices, and speak with adjusters for claims. Yet technology is transforming the way insurance carriers provide coverage and how policyholders receive service. Technological advancements are starting to automate and predict standard insurance-related tasks, from filing a claim to adjusting a policy's coverage. As the industry embraces things like artificial intelligence, machine learning, and other technologies, the relationship between providers and clients is also changing.

The study of robotic flocking has received significant attention in the past twenty years. In this article, we present a constraint-driven control algorithm that minimizes the energy consumption of individual agents and yields an emergent V formation. As the formation emerges from the decentralized interaction between agents, our approach is robust to the spontaneous addition or removal of agents to the system. First, we present an analytical model for the trailing upwash behind a fixed-wing UAV, and we derive the optimal air speed for trailing UAVs to maximize their travel endurance. Next, we prove that simply flying at the optimal airspeed will never lead to emergent flocking behavior, and we propose a new decentralized "anseroid" behavior that yields emergent V formations. We encode these behaviors in a constraint-driven control algorithm that minimizes the locomotive power of each UAV. Finally, we prove that UAVs initialized in an approximate V or echelon formation will converge under our proposed control law, and we demonstrate this emergence occurs in real-time in simulation and in physical experiments with a fleet of Crazyflie quadrotors.

We propose a novel hybrid system (both hardware and software) of an Unmanned Aerial Vehicle (UAV) carrying a miniature Unmanned Ground Vehicle (miniUGV) to perform a complex search and manipulation task. This system leverages heterogeneous robots to accomplish a task that cannot be done using a single robot system. It enables the UAV to explore a hidden space with a narrow opening through which the miniUGV can easily enter and escape. The hidden space is assumed to be navigable for the miniUGV. The miniUGV uses Infrared (IR) sensors and a monocular camera to search for an object in the hidden space. The proposed system takes advantage of a wider field of view (fov) of the camera as well as the stochastic nature of the object detection algorithms to guide the miniUGV in the hidden space to find the object. Upon finding the object the miniUGV grabs it using visual servoing and then returns back to its start point from where the UAV retracts it back and transports the object to a safe place. In case there is no object found in the hidden space, UAV continues the aerial search. The tethered miniUGV gives the UAV an ability to act beyond its reach and perform a search and manipulation task which was not possible before for any of the robots individually. The system has a wide range of applications and we have demonstrated its feasibility through repetitive experiments.

We present a multirotor Unmanned Aerial Vehicle control (UAV) and estimation system for supporting replicable research through realistic simulations and real-world experiments. We propose a unique multi-frame localization paradigm for estimating the states of a UAV in various frames of reference using multiple sensors simultaneously. The system enables complex missions in GNSS and GNSS-denied environments, including outdoor-indoor transitions and the execution of redundant estimators for backing up unreliable localization sources. Two feedback control designs are presented: one for precise and aggressive maneuvers, and the other for stable and smooth flight with a noisy state estimate. The proposed control and estimation pipeline are constructed without using the Euler/Tait-Bryan angle representation of orientation in 3D. Instead, we rely on rotation matrices and a novel heading-based convention to represent the one free rotational degree-of-freedom in 3D of a standard multirotor helicopter. We provide an actively maintained and well-documented open-source implementation, including realistic simulation of UAV, sensors, and localization systems. The proposed system is the product of years of applied research on multi-robot systems, aerial swarms, aerial manipulation, motion planning, and remote sensing. All our results have been supported by real-world system deployment that shaped the system into the form presented here. In addition, the system was utilized during the participation of our team from the CTU in Prague in the prestigious MBZIRC 2017 and 2020 robotics competitions, and also in the DARPA SubT challenge. Each time, our team was able to secure top places among the best competitors from all over the world. On each occasion, the challenges has motivated the team to improve the system and to gain a great amount of high-quality experience within tight deadlines.

We consider the task of detecting anomalies for autonomous mobile robots based on vision. We categorize relevant types of visual anomalies and discuss how they can be detected by unsupervised deep learning methods. We propose a novel dataset built specifically for this task, on which we test a state-of-the-art approach; we finally discuss deployment in a real scenario.

Reinforcement learning (RL) is an agent-based approach for teaching robots to navigate within the physical world. Gathering data for RL is known to be a laborious task, and real-world experiments can be risky. Simulators facilitate the collection of training data in a quicker and more cost-effective manner. However, RL frequently requires a significant number of simulation steps for an agent to become skilful at simple tasks. This is a prevalent issue within the field of RL-based visual quadrotor navigation where state dimensions are typically very large and dynamic models are complex. Furthermore, rendering images and obtaining physical properties of the agent can be computationally expensive. To solve this, we present a simulation framework, built on AirSim, which provides efficient parallel training. Building on this framework, Ape-X is modified to incorporate decentralised training of AirSim environments to make use of numerous networked computers. Through experiments we were able to achieve a reduction in training time from 3.9 hours to 11 minutes using the aforementioned framework and a total of 74 agents and two networked computers. Further details including a github repo and videos about our project, PRL4AirSim, can be found at https://sites.google.com/view/prl4airsim/home

Metamorphic testing has proven to be effective for test case generation and fault detection in many domains. It is a software testing strategy that uses certain relations between input-output pairs of a program, referred to as metamorphic relations. This approach is relevant in the autonomous systems domain since it helps in cases where the outcome of a given test input may be difficult to determine. In this paper therefore, we provide an overview of metamorphic testing as well as an implementation in the autonomous systems domain. We implement an obstacle detection and avoidance task in autonomous drones utilising the GNC API alongside a simulation in Gazebo. Particularly, we describe properties and best practices that are crucial for the development of effective metamorphic relations. We also demonstrate two metamorphic relations for metamorphic testing of single and more than one drones, respectively. Our relations reveal several properties and some weak spots of both the implementation and the avoidance algorithm in the light of metamorphic testing. The results indicate that metamorphic testing has great potential in the autonomous systems domain and should be considered for quality assurance in this field.

To achieve high fidelity haptic rendering of soft objects in a high mobility virtual environment, we propose a novel haptic display DandelionTouch. The tactile actuators are delivered to the fingertips of the user by a swarm of drones. Users of DandelionTouch are capable of experiencing tactile feedback in a large space that is not limited by the device's working area. Importantly, they will not experience muscle fatigue during long interactions with virtual objects. Hand tracking and swarm control algorithm allow guiding the swarm with hand motions and avoid collisions inside the formation. Several topologies of the impedance connection between swarm units were investigated in this research. The experiment, in which drones performed a point following task on a square trajectory in real-time, revealed that drones connected in a Star topology performed the trajectory with low mean positional error (RMSE decreased by 20.6% in comparison with other impedance topologies and by 40.9% in comparison with potential field-based swarm control). The achieved velocities of the drones in all formations with impedance behavior were 28% higher than for the swarm controlled with the potential field algorithm. Additionally, the perception of several vibrotactile patterns was evaluated in a user study with 7 participants. The study has shown that the proposed combination of temporal delay and frequency modulation allows users to successfully recognize the surface property and motion direction in VR simultaneously (mean recognition rate of 70%, maximum of 93%). DandelionTouch suggests a new type of haptic feedback in VR systems where no hand-held or wearable interface is required.

Off-road autonomous unmanned ground vehicles (UGVs) are being developed for military and commercial use to deliver crucial supplies in remote locations, help with mapping and surveillance, and to assist war-fighters in contested environments. Due to complexity of the off-road environments and variability in terrain, lighting conditions, diurnal and seasonal changes, the models used to perceive the environment must handle a lot of input variability. Current datasets used to train perception models for off-road autonomous navigation lack of diversity in seasons, locations, semantic classes, as well as time of day. We test the hypothesis that model trained on a single dataset may not generalize to other off-road navigation datasets and new locations due to the input distribution drift. Additionally, we investigate how to combine multiple datasets to train a semantic segmentation-based environment perception model and we show that training the model to capture uncertainty could improve the model performance by a significant margin. We extend the Masksembles approach for uncertainty quantification to the semantic segmentation task and compare it with Monte Carlo Dropout and standard baselines. Finally, we test the approach against data collected from a UGV platform in a new testing environment. We show that the developed perception model with uncertainty quantification can be feasibly deployed on an UGV to support online perception and navigation tasks.